NDLI: Sart-Type Half-Threshold Filtering Approach for CT Reconstruction

Content Provider	IEEE Xplore Digital Library
Author	Hengyong Yu Ge Wang
Copyright Year	2013
Abstract	The ℓ₁ regularization problem has been widely used to solve the sparsity constrained problems. To enhance the sparsity constraint for better imaging performance, a promising direction is to use the ℓ_p norm (0 <; p <; 1) and solve the ℓ_p minimization problem. Very recently, Xu et al. developed an analytic solution for the ℓ_1/2 regularization via an iterative thresholding operation, which is also referred to as half-threshold filtering. In this paper, we design a simultaneous algebraic reconstruction technique (SART)-type half-threshold filtering framework to solve the computed tomography (CT) reconstruction problem. In the medical imaging filed, the discrete gradient transform (DGT) is widely used to define the sparsity. However, the DGT is noninvertible and it cannot be applied to half-threshold filtering for CT reconstruction. To demonstrate the utility of the proposed SART-type half-threshold filtering framework, an emphasis of this paper is to construct a pseudoinverse transforms for DGT. The proposed algorithms are evaluated with numerical and physical phantom data sets. Our results show that the SART-type half-threshold filtering algorithms have great potential to improve the reconstructed image quality from few and noisy projections. They are complementary to the counterparts of the state-of-the-art soft-threshold filtering and hard-threshold filtering.
Starting Page	602
Ending Page	613
Page Count	12
File Size	4706461
File Format	PDF
ISSN	21693536
Volume Number	2
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-01-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Filtering Image reconstruction Transforms Noise measurement Computed tomography Minization Threshold analysis Biomedical measurement pseudo-inverse transform Compressive sampling discrete gradient transform half-threshold filtering
Content Type	Text
Resource Type	Article
Subject	Computer Science Engineering Materials Science

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